BirdNET-Analyzer and birdnet-onnx-converter
BirdNET-Analyzer is the primary species classification framework, while the ONNX converter is a complementary optimization tool that enables deployment of BirdNET models across diverse hardware platforms by converting them to ONNX format for inference efficiency.
About BirdNET-Analyzer
birdnet-team/BirdNET-Analyzer
BirdNET analyzer for scientific audio data processing.
Leverages deep learning models trained on 6,512+ bird species to automatically detect and classify avian vocalizations in audio files or continuous streams. Provides both command-line and GUI interfaces designed for researchers without CS expertise, with support for batch processing large audio datasets and real-time analysis through Docker containerization. Integrates with Zenodo for model distribution and supports cross-platform deployment on Linux, Windows, and macOS via native installers or Python package management.
About birdnet-onnx-converter
tphakala/birdnet-onnx-converter
Convert and optimize BirdNET models for ONNX Runtime inference on GPUs, CPUs, and embedded devices
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work